zhudelong / crp

C++ Implementation of Customizable Route Planning (CRP) by Delling et al.

CRP

Open source C++ Implementation of Customizable Route Planning (CRP) by Delling et al. This project was part of a practical course at Karlsruhe Institute of Technology (KIT).

Requirements

In order to build CRP you need to have the following software installed:

• Boost C++ Library (http://www.boost.org), more specifically Boost Iostreams.
• Scons (http://scons.org)
• g++ >= 4.8 (https://gcc.gnu.org)

Building CRP

If the Boost Library is not in your PATH, make sure to edit the SConstruct file in the root directory to point the build script to the correct location of Boost. There is a section Libraries in the SConstruct file where you can specify the paths.

Once you have installed all the software packages listed above, you can build the CRP programs by typing

scons --target=CRP --optimize=Opt -jX

into your terminal where X is the number of cores you want to use for building the project. If you want to use a specific g++ compiler version you can add --compiler=g++-Version. We also support a debug and profiling build that you can call with --optimize=Dbg and --optimize=Pro respectively.

This command will build three programs in the folder deploy:

• osmparser: Used to parse an OpenStreetMap (OSM) bz2-compressed map file. Call it with ./deploy/osmparser path_to_osm.bz2 path_to_output.graph.bz2
• precalculation: Used to build an overlay graph based on a given partition. Call it with ./deploy/precalculation path_to_graph path_to_mlp output_directory. Here, path_to_mlp is the path to a MultiLevelPartition file for the graph that you need to provide. For more details, take a look into our project documentation.
• customization: Used to precompute the metric weights for the overlay graph. Call it with ./deploy/customization path_to_graph path_to_overlay_graph metric_output_directory metric_type. We currently support the following metric types: hop (number of edges traversed), time and dist. You can compute all metrics with all as metric_type.

Example

Start by calling

./deploy/osmparser examples/karlsruhe/karlsruhe.osm.bz2 examples/karlsruhe/karlsruhe.graph.bz2

This will parse the osm map data for the city of Karlsruhe, Germany and writes the extracted graph.

The next step would be to compute a MultiLevelPartition for the graph. For this example, we already provide one and can directly continue with computing the overlay graph:

./deploy/precalculation examples/karlsruhe/karlsruhe.graph.bz2 examples/karlsruhe/karlsruhe.mlp  examples/karlsruhe/

Note that specifying the same folder as output directory (like in this example) will overwrite the initial graph with a new one containing additional information. Since we do some vertex sorting in this step, you cannot call precalculation on this graph anymore (i.e. first run the osmparser again or choose a different output directory so that the original graph still exists).

In a final step we run the customization phase to build the metric information for the overlay graph:

mkdir examples/karlsruhe/metrics
./deploy/customization examples/karlsruhe/karlsruhe.graph.bz2 examples/karlsruhe/karlsruhe.overlay examples/karlsruhe/metrics/ all

This completes the precomputation steps and CRP is now ready to compute shortest paths.

Building the Tests

You can build some tests to see how CRP performs with the following command:

scons --target=TEST --optimize=Opt

where TEST can be one of the following: QueryTest (runs our three available query algorithms), UnpackPathTest (checks the performance of the PathUnpacker), DijkstraTest (checks that our query algorithms work as expected) and OverlayGraphTest (builds a small overlay graph and performs some sanity tests on it).

The list of required parameters is printed to the terminal by calling the built test program in the deploy folder without any additional arguments.